The study evaluates how feature engineering (FE) affects machine learning models for ocean colour data, proposing a seven‑step optimisation framework that includes band selection, scaling, normalisation, index extraction, PCA, and feature scaling. Applied to Sentinel‑3 OLCI observations, the framework improves model accuracy for estimating Chlorophyll‑a and Secchi disk depth, achieving higher R values and lower mean absolute errors compared to standard algorithms. However, the optimal FE varies across targets and models, indicating that FE optimisation must be tailored to each application.
arXiv:2607. 15775v1 Announce Type: cross Abstract: Access to potable water is crucial for health, economic development, and sustainability.
By Muntasir Tabasum, Al Zadid Sultan Bin Habib, Tanpia Tasnim, Md. Ekramul Islam, Md Younus Ahamed, Md Asif Bin Syed
arXiv:2609.13206v1 Announce Type: cross
Abstract: Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such...
By Makayla McDevitt, Maike Sonnewald, Stephanie Dutkiewicz
arXiv:2608. 14935v1 Announce Type: cross Abstract: Turbulent fluxes between the surface and the atmosphere are typically parameterized using empirically fit relationships.
By Susan Dettling, Sue Ellen Haupt, Thomas Brummet, Patrick Hawbecker, Branko Kosovi\'c, David John Gagne
arXiv:2609.12744v1 Announce Type: new
Abstract: Timely monitoring of coastal water quality is critical for environmental protection, yet conventional satellite workflows rely on downlink and ground p...
By Pietro Di Stasio, Francesca Razzano, Elisa Liparulo, Gabriele Meoni, Nicolas Long\'ep\'e, Deodato Tapete, Paolo Gamba, Gilda Schirinzi, Silvia Liberata Ullo
arXiv:2605. 24003v2 Announce Type: replace-cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years.
By Shuang Liu, Fiona Johnson, Rohitash Chandra